Overview

Dataset statistics

Number of variables40
Number of observations2459560
Missing cells48623585
Missing cells (%)49.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 GiB
Average record size in memory1.1 KiB

Variable types

Categorical16
Numeric17
Unsupported7

Alerts

ancien_code_commune has constant value "49382.0" Constant
ancien_nom_commune has constant value "Le Fresne-sur-Loire" Constant
ancien_id_parcelle has constant value "440600000B1194" Constant
id_mutation has a high cardinality: 1075292 distinct values High cardinality
date_mutation has a high cardinality: 360 distinct values High cardinality
adresse_nom_voie has a high cardinality: 394478 distinct values High cardinality
nom_commune has a high cardinality: 29092 distinct values High cardinality
id_parcelle has a high cardinality: 1535306 distinct values High cardinality
code_nature_culture_speciale has a high cardinality: 120 distinct values High cardinality
nature_culture_speciale has a high cardinality: 120 distinct values High cardinality
lot1_surface_carrez is highly correlated with lot4_surface_carrez and 3 other fieldsHigh correlation
lot2_surface_carrez is highly correlated with surface_reelle_bati and 1 other fieldsHigh correlation
lot3_surface_carrez is highly correlated with lot4_surface_carrez and 1 other fieldsHigh correlation
lot4_numero is highly correlated with lot5_numeroHigh correlation
lot4_surface_carrez is highly correlated with lot1_surface_carrez and 2 other fieldsHigh correlation
lot5_numero is highly correlated with lot4_numeroHigh correlation
lot5_surface_carrez is highly correlated with lot4_surface_carrezHigh correlation
nombre_lots is highly correlated with code_type_localHigh correlation
code_type_local is highly correlated with nombre_lots and 1 other fieldsHigh correlation
surface_reelle_bati is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
nombre_pieces_principales is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
surface_terrain is highly correlated with lot1_surface_carrezHigh correlation
valeur_fonciere is highly correlated with lot5_surface_carrezHigh correlation
lot1_surface_carrez is highly correlated with lot2_surface_carrez and 3 other fieldsHigh correlation
lot2_surface_carrez is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
lot3_surface_carrez is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
lot4_numero is highly correlated with lot5_numeroHigh correlation
lot4_surface_carrez is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
lot5_numero is highly correlated with lot4_numeroHigh correlation
lot5_surface_carrez is highly correlated with valeur_fonciere and 4 other fieldsHigh correlation
code_type_local is highly correlated with nombre_pieces_principalesHigh correlation
nombre_pieces_principales is highly correlated with code_type_localHigh correlation
lot1_surface_carrez is highly correlated with surface_reelle_bati and 1 other fieldsHigh correlation
lot2_surface_carrez is highly correlated with surface_reelle_bati and 1 other fieldsHigh correlation
lot3_surface_carrez is highly correlated with lot4_surface_carrez and 1 other fieldsHigh correlation
lot4_numero is highly correlated with lot5_numeroHigh correlation
lot4_surface_carrez is highly correlated with lot3_surface_carrezHigh correlation
lot5_numero is highly correlated with lot4_numeroHigh correlation
code_type_local is highly correlated with nombre_pieces_principalesHigh correlation
surface_reelle_bati is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
nombre_pieces_principales is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
adresse_numero is highly correlated with adresse_suffixe and 1 other fieldsHigh correlation
adresse_suffixe is highly correlated with adresse_numeroHigh correlation
lot1_surface_carrez is highly correlated with lot2_surface_carrez and 5 other fieldsHigh correlation
lot2_surface_carrez is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
lot3_surface_carrez is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
lot4_numero is highly correlated with lot5_numero and 1 other fieldsHigh correlation
lot4_surface_carrez is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
lot5_numero is highly correlated with lot4_numero and 2 other fieldsHigh correlation
lot5_surface_carrez is highly correlated with adresse_numero and 6 other fieldsHigh correlation
nombre_lots is highly correlated with lot5_numeroHigh correlation
code_type_local is highly correlated with type_localHigh correlation
type_local is highly correlated with code_type_localHigh correlation
code_nature_culture is highly correlated with lot1_surface_carrez and 1 other fieldsHigh correlation
nature_culture is highly correlated with lot1_surface_carrez and 1 other fieldsHigh correlation
longitude is highly correlated with latitudeHigh correlation
latitude is highly correlated with longitudeHigh correlation
valeur_fonciere has 31752 (1.3%) missing values Missing
adresse_numero has 990345 (40.3%) missing values Missing
adresse_suffixe has 2352374 (95.6%) missing values Missing
adresse_nom_voie has 31123 (1.3%) missing values Missing
adresse_code_voie has 30948 (1.3%) missing values Missing
code_postal has 30992 (1.3%) missing values Missing
ancien_code_commune has 2459559 (> 99.9%) missing values Missing
ancien_nom_commune has 2459559 (> 99.9%) missing values Missing
ancien_id_parcelle has 2459559 (> 99.9%) missing values Missing
numero_volume has 2452794 (99.7%) missing values Missing
lot1_numero has 1702571 (69.2%) missing values Missing
lot1_surface_carrez has 2250280 (91.5%) missing values Missing
lot2_numero has 2303817 (93.7%) missing values Missing
lot2_surface_carrez has 2408519 (97.9%) missing values Missing
lot3_numero has 2434251 (99.0%) missing values Missing
lot3_surface_carrez has 2454703 (99.8%) missing values Missing
lot4_numero has 2450649 (99.6%) missing values Missing
lot4_surface_carrez has 2458303 (99.9%) missing values Missing
lot5_numero has 2455417 (99.8%) missing values Missing
lot5_surface_carrez has 2458997 (> 99.9%) missing values Missing
code_type_local has 1143726 (46.5%) missing values Missing
type_local has 1143726 (46.5%) missing values Missing
surface_reelle_bati has 1447059 (58.8%) missing values Missing
nombre_pieces_principales has 1145290 (46.6%) missing values Missing
code_nature_culture has 767675 (31.2%) missing values Missing
nature_culture has 767675 (31.2%) missing values Missing
code_nature_culture_speciale has 2352011 (95.6%) missing values Missing
nature_culture_speciale has 2352011 (95.6%) missing values Missing
surface_terrain has 767734 (31.2%) missing values Missing
longitude has 30083 (1.2%) missing values Missing
latitude has 30083 (1.2%) missing values Missing
numero_disposition is highly skewed (γ1 = 43.49084046) Skewed
valeur_fonciere is highly skewed (γ1 = 33.82295023) Skewed
lot1_surface_carrez is highly skewed (γ1 = 38.30939309) Skewed
lot2_surface_carrez is highly skewed (γ1 = 61.88484225) Skewed
lot3_surface_carrez is highly skewed (γ1 = 34.54675199) Skewed
lot4_surface_carrez is highly skewed (γ1 = 20.40788883) Skewed
nombre_lots is highly skewed (γ1 = 22.11245427) Skewed
surface_reelle_bati is highly skewed (γ1 = 121.3804483) Skewed
surface_terrain is highly skewed (γ1 = 245.0111668) Skewed
adresse_code_voie is an unsupported type, check if it needs cleaning or further analysis Unsupported
code_commune is an unsupported type, check if it needs cleaning or further analysis Unsupported
code_departement is an unsupported type, check if it needs cleaning or further analysis Unsupported
numero_volume is an unsupported type, check if it needs cleaning or further analysis Unsupported
lot1_numero is an unsupported type, check if it needs cleaning or further analysis Unsupported
lot2_numero is an unsupported type, check if it needs cleaning or further analysis Unsupported
lot3_numero is an unsupported type, check if it needs cleaning or further analysis Unsupported
nombre_lots has 1702571 (69.2%) zeros Zeros
nombre_pieces_principales has 385324 (15.7%) zeros Zeros

Reproduction

Analysis started2021-09-28 12:16:22.932386
Analysis finished2021-09-28 12:28:16.051425
Duration11 minutes and 53.12 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

id_mutation
Categorical

HIGH CARDINALITY

Distinct1075292
Distinct (%)43.7%
Missing0
Missing (%)0.0%
Memory size159.4 MiB
2020-623209
 
9147
2020-1015900
 
1630
2020-793983
 
859
2020-913532
 
828
2020-294233
 
748
Other values (1075287)
2446348 

Length

Max length12
Median length11
Mean length10.94772724
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique515782 ?
Unique (%)21.0%

Sample

1st row2020-1
2nd row2020-2
3rd row2020-2
4th row2020-2
5th row2020-2

Common Values

ValueCountFrequency (%)
2020-6232099147
 
0.4%
2020-10159001630
 
0.1%
2020-793983859
 
< 0.1%
2020-913532828
 
< 0.1%
2020-294233748
 
< 0.1%
2020-948402746
 
< 0.1%
2020-800307729
 
< 0.1%
2020-1019571659
 
< 0.1%
2020-1017225602
 
< 0.1%
2020-41662581
 
< 0.1%
Other values (1075282)2443031
99.3%

Length

2021-09-28T14:28:16.336424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-6232099147
 
0.4%
2020-10159001630
 
0.1%
2020-793983859
 
< 0.1%
2020-913532828
 
< 0.1%
2020-294233748
 
< 0.1%
2020-948402746
 
< 0.1%
2020-800307729
 
< 0.1%
2020-1019571659
 
< 0.1%
2020-1017225602
 
< 0.1%
2020-41662581
 
< 0.1%
Other values (1075282)2443031
99.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

date_mutation
Categorical

HIGH CARDINALITY

Distinct360
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.2 MiB
2020-05-29
 
24636
2020-01-31
 
21218
2020-06-30
 
20464
2020-02-28
 
19335
2020-05-15
 
19317
Other values (355)
2354590 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row2020-01-07
2nd row2020-01-02
3rd row2020-01-02
4th row2020-01-02
5th row2020-01-02

Common Values

ValueCountFrequency (%)
2020-05-2924636
 
1.0%
2020-01-3121218
 
0.9%
2020-06-3020464
 
0.8%
2020-02-2819335
 
0.8%
2020-05-1519317
 
0.8%
2020-07-3118301
 
0.7%
2020-05-2018198
 
0.7%
2020-09-3017835
 
0.7%
2020-11-0917222
 
0.7%
2020-12-1817208
 
0.7%
Other values (350)2265826
92.1%

Length

2021-09-28T14:28:16.643426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-05-2924636
 
1.0%
2020-01-3121218
 
0.9%
2020-06-3020464
 
0.8%
2020-02-2819335
 
0.8%
2020-05-1519317
 
0.8%
2020-07-3118301
 
0.7%
2020-05-2018198
 
0.7%
2020-09-3017835
 
0.7%
2020-11-0917222
 
0.7%
2020-12-1817208
 
0.7%
Other values (350)2265826
92.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

numero_disposition
Real number (ℝ≥0)

SKEWED

Distinct74
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.075359007
Minimum1
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.8 MiB
2021-09-28T14:28:16.949953image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum74
Range73
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5593660002
Coefficient of variation (CV)0.5201667503
Kurtosis3466.948447
Mean1.075359007
Median Absolute Deviation (MAD)0
Skewness43.49084046
Sum2644910
Variance0.3128903222
MonotonicityNot monotonic
2021-09-28T14:28:17.330953image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12326887
94.6%
2105673
 
4.3%
322299
 
0.9%
42094
 
0.1%
5663
 
< 0.1%
6319
 
< 0.1%
9199
 
< 0.1%
7184
 
< 0.1%
8152
 
< 0.1%
18146
 
< 0.1%
Other values (64)944
 
< 0.1%
ValueCountFrequency (%)
12326887
94.6%
2105673
 
4.3%
322299
 
0.9%
42094
 
0.1%
5663
 
< 0.1%
6319
 
< 0.1%
7184
 
< 0.1%
8152
 
< 0.1%
9199
 
< 0.1%
10105
 
< 0.1%
ValueCountFrequency (%)
743
< 0.1%
733
< 0.1%
721
 
< 0.1%
711
 
< 0.1%
701
 
< 0.1%
692
< 0.1%
681
 
< 0.1%
672
< 0.1%
661
 
< 0.1%
651
 
< 0.1%

nature_mutation
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size161.5 MiB
Vente
2245097 
Vente en l'état futur d'achèvement
 
181174
Echange
 
23125
Vente terrain à bâtir
 
6814
Adjudication
 
2744

Length

Max length34
Median length5
Mean length7.209084552
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVente
2nd rowVente
3rd rowVente
4th rowVente
5th rowVente

Common Values

ValueCountFrequency (%)
Vente2245097
91.3%
Vente en l'état futur d'achèvement181174
 
7.4%
Echange23125
 
0.9%
Vente terrain à bâtir6814
 
0.3%
Adjudication2744
 
0.1%
Expropriation606
 
< 0.1%

Length

2021-09-28T14:28:17.660006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-28T14:28:17.861006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
vente2433085
75.9%
en181174
 
5.7%
l'état181174
 
5.7%
futur181174
 
5.7%
d'achèvement181174
 
5.7%
echange23125
 
0.7%
terrain6814
 
0.2%
à6814
 
0.2%
bâtir6814
 
0.2%
adjudication2744
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

valeur_fonciere
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED

Distinct109008
Distinct (%)4.5%
Missing31752
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean751173.5185
Minimum0.15
Maximum490000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.8 MiB
2021-09-28T14:28:18.150009image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.15
5-th percentile3400
Q165000
median155000
Q3275706.34
95-th percentile1041400
Maximum490000000
Range489999999.9
Interquartile range (IQR)210706.34

Descriptive statistics

Standard deviation7687440.578
Coefficient of variation (CV)10.23390786
Kurtosis1527.457106
Mean751173.5185
Median Absolute Deviation (MAD)100000
Skewness33.82295023
Sum1.823705078 × 1012
Variance5.909674265 × 1013
MonotonicityNot monotonic
2021-09-28T14:28:18.495004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15000020868
 
0.8%
10000020027
 
0.8%
12000018832
 
0.8%
20000017509
 
0.7%
8000017303
 
0.7%
13000016659
 
0.7%
9000016409
 
0.7%
14000016400
 
0.7%
5000016369
 
0.7%
11000015879
 
0.6%
Other values (108998)2251553
91.5%
(Missing)31752
 
1.3%
ValueCountFrequency (%)
0.15135
< 0.1%
0.168
 
< 0.1%
0.183
 
< 0.1%
0.21
 
< 0.1%
0.38
 
< 0.1%
0.510
 
< 0.1%
0.64
 
< 0.1%
0.71
 
< 0.1%
0.741
 
< 0.1%
0.81
 
< 0.1%
ValueCountFrequency (%)
49000000037
 
< 0.1%
4780905283
 
< 0.1%
4587320321
 
< 0.1%
436035040136
< 0.1%
4352449921
 
< 0.1%
4190000002
 
< 0.1%
401049248134
< 0.1%
3620318402
 
< 0.1%
3266110084
 
< 0.1%
3124419203
 
< 0.1%

adresse_numero
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct6645
Distinct (%)0.5%
Missing990345
Missing (%)40.3%
Infinite0
Infinite (%)0.0%
Mean697.7083374
Minimum1
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.8 MiB
2021-09-28T14:28:18.869004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median24
Q388
95-th percentile5508
Maximum9999
Range9998
Interquartile range (IQR)80

Descriptive statistics

Standard deviation2017.513768
Coefficient of variation (CV)2.891629151
Kurtosis8.807717566
Mean697.7083374
Median Absolute Deviation (MAD)20
Skewness3.143063121
Sum1025083555
Variance4070361.803
MonotonicityNot monotonic
2021-09-28T14:28:19.215004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
166120
 
2.7%
259943
 
2.4%
350285
 
2.0%
447324
 
1.9%
543429
 
1.8%
641342
 
1.7%
736576
 
1.5%
836268
 
1.5%
934058
 
1.4%
1033382
 
1.4%
Other values (6635)1020488
41.5%
(Missing)990345
40.3%
ValueCountFrequency (%)
166120
2.7%
259943
2.4%
350285
2.0%
447324
1.9%
543429
1.8%
641342
1.7%
736576
1.5%
836268
1.5%
934058
1.4%
1033382
1.4%
ValueCountFrequency (%)
9999427
< 0.1%
999852
 
< 0.1%
999713
 
< 0.1%
999620
 
< 0.1%
999514
 
< 0.1%
99943
 
< 0.1%
99934
 
< 0.1%
999111
 
< 0.1%
999017
 
< 0.1%
99893
 
< 0.1%

adresse_suffixe
Categorical

HIGH CORRELATION
MISSING

Distinct38
Distinct (%)< 0.1%
Missing2352374
Missing (%)95.6%
Memory size77.7 MiB
B
64394 
A
16399 
T
8870 
F
7699 
C
 
3329
Other values (33)
6495 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
B64394
 
2.6%
A16399
 
0.7%
T8870
 
0.4%
F7699
 
0.3%
C3329
 
0.1%
D1656
 
0.1%
Q994
 
< 0.1%
E922
 
< 0.1%
P663
 
< 0.1%
G383
 
< 0.1%
Other values (28)1877
 
0.1%
(Missing)2352374
95.6%

Length

2021-09-28T14:28:19.532004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b64394
60.1%
a16399
 
15.3%
t8870
 
8.3%
f7699
 
7.2%
c3329
 
3.1%
d1656
 
1.5%
q994
 
0.9%
e922
 
0.9%
p663
 
0.6%
g383
 
0.4%
Other values (27)1877
 
1.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

adresse_nom_voie
Categorical

HIGH CARDINALITY
MISSING

Distinct394478
Distinct (%)16.2%
Missing31123
Missing (%)1.3%
Memory size167.3 MiB
LE VILLAGE
 
25599
LE BOURG
 
21432
AV JEAN JAURES
 
5626
GR GRANDE RUE
 
5215
RUE JEAN JAURES
 
4911
Other values (394473)
2365654 

Length

Max length31
Median length15
Mean length14.84831313
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique141229 ?
Unique (%)5.8%

Sample

1st rowFORTUNAT
2nd rowTERRES DES CINQ SAULES
3rd rowBOIS DU CHAMP RION
4th rowEN COROBERT
5th rowTERRES DES CINQ SAULES

Common Values

ValueCountFrequency (%)
LE VILLAGE25599
 
1.0%
LE BOURG21432
 
0.9%
AV JEAN JAURES5626
 
0.2%
GR GRANDE RUE5215
 
0.2%
RUE JEAN JAURES4911
 
0.2%
RUE DE LA REPUBLIQUE4687
 
0.2%
RUE PASTEUR4504
 
0.2%
RUE DE PARIS4088
 
0.2%
RUE VICTOR HUGO3641
 
0.1%
AV DE LA REPUBLIQUE3560
 
0.1%
Other values (394468)2345174
95.3%
(Missing)31123
 
1.3%

Length

2021-09-28T14:28:19.936009image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rue852393
 
12.1%
de550864
 
7.8%
la378179
 
5.4%
du258747
 
3.7%
le219407
 
3.1%
des212473
 
3.0%
av186380
 
2.6%
les162604
 
2.3%
rte78517
 
1.1%
che73448
 
1.0%
Other values (169029)4092282
57.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

adresse_code_voie
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing30948
Missing (%)1.3%
Memory size142.2 MiB

code_postal
Real number (ℝ≥0)

MISSING

Distinct5803
Distinct (%)0.2%
Missing30992
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean51783.62872
Minimum1000
Maximum97490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.8 MiB
2021-09-28T14:28:20.257006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile6400
Q131250
median50700
Q376600
95-th percentile93250
Maximum97490
Range96490
Interquartile range (IQR)45350

Descriptive statistics

Standard deviation27241.41056
Coefficient of variation (CV)0.5260622176
Kurtosis-1.123563097
Mean51783.62872
Median Absolute Deviation (MAD)23460
Skewness-0.07783709677
Sum1.257600636 × 1011
Variance742094449.3
MonotonicityNot monotonic
2021-09-28T14:28:20.566007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
511006969
 
0.3%
540006252
 
0.3%
350005829
 
0.2%
750165390
 
0.2%
60005341
 
0.2%
750155283
 
0.2%
443005000
 
0.2%
750184992
 
0.2%
62004885
 
0.2%
720004598
 
0.2%
Other values (5793)2374029
96.5%
(Missing)30992
 
1.3%
ValueCountFrequency (%)
1000900
< 0.1%
1090437
 
< 0.1%
1100872
< 0.1%
1110390
 
< 0.1%
1120877
< 0.1%
1130295
 
< 0.1%
1140440
 
< 0.1%
1150963
< 0.1%
1160427
 
< 0.1%
11701567
0.1%
ValueCountFrequency (%)
97490354
< 0.1%
97480393
< 0.1%
97470158
< 0.1%
97460161
< 0.1%
97450336
< 0.1%
9744272
 
< 0.1%
97441105
 
< 0.1%
97440202
< 0.1%
9743932
 
< 0.1%
97438109
 
< 0.1%

code_commune
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size85.3 MiB

nom_commune
Categorical

HIGH CARDINALITY

Distinct29092
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size181.4 MiB
Nice
 
17756
Nantes
 
14715
Bordeaux
 
12847
Lille
 
10628
Rennes
 
9721
Other values (29087)
2393893 

Length

Max length45
Median length10
Mean length11.94212827
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique694 ?
Unique (%)< 0.1%

Sample

1st rowCeyzériat
2nd rowLaiz
3rd rowLaiz
4th rowLaiz
5th rowLaiz

Common Values

ValueCountFrequency (%)
Nice17756
 
0.7%
Nantes14715
 
0.6%
Bordeaux12847
 
0.5%
Lille10628
 
0.4%
Rennes9721
 
0.4%
Toulouse8854
 
0.4%
Nîmes8571
 
0.3%
Saint-Étienne7400
 
0.3%
Reims7110
 
0.3%
Angers6642
 
0.3%
Other values (29082)2355316
95.8%

Length

2021-09-28T14:28:20.936004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
arrondissement85812
 
3.0%
le75906
 
2.7%
la74081
 
2.6%
paris54130
 
1.9%
les27956
 
1.0%
marseille20900
 
0.7%
nice17756
 
0.6%
nantes14715
 
0.5%
bordeaux12847
 
0.4%
lyon10782
 
0.4%
Other values (29013)2460220
86.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

code_departement
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size85.2 MiB

ancien_code_commune
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)100.0%
Missing2459559
Missing (%)> 99.9%
Memory size93.8 MiB
49382.0

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row49382.0

Common Values

ValueCountFrequency (%)
49382.01
 
< 0.1%
(Missing)2459559
> 99.9%

Length

2021-09-28T14:28:21.205003image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-28T14:28:21.343007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
49382.01
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ancien_nom_commune
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)100.0%
Missing2459559
Missing (%)> 99.9%
Memory size75.1 MiB
Le Fresne-sur-Loire

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowLe Fresne-sur-Loire

Common Values

ValueCountFrequency (%)
Le Fresne-sur-Loire1
 
< 0.1%
(Missing)2459559
> 99.9%

Length

2021-09-28T14:28:21.663003image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-28T14:28:21.812003image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
le1
50.0%
fresne-sur-loire1
50.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

id_parcelle
Categorical

HIGH CARDINALITY

Distinct1535306
Distinct (%)62.4%
Missing0
Missing (%)0.0%
Memory size166.5 MiB
78165000AO0008
 
858
92004000AZ0011
 
746
30189000EM0022
 
705
78440000AR0388
 
687
62193000XB0006
 
648
Other values (1535301)
2455916 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1221123 ?
Unique (%)49.6%

Sample

1st row01072000AK0216
2nd row012030000B0004
3rd row012030000B0173
4th row012030000B0477
5th row012030000C0068

Common Values

ValueCountFrequency (%)
78165000AO0008858
 
< 0.1%
92004000AZ0011746
 
< 0.1%
30189000EM0022705
 
< 0.1%
78440000AR0388687
 
< 0.1%
62193000XB0006648
 
< 0.1%
06088000IM0369576
 
< 0.1%
940330000I0670476
 
< 0.1%
06088000OB0437426
 
< 0.1%
76351000EI0024425
 
< 0.1%
920040000M0071401
 
< 0.1%
Other values (1535296)2453612
99.8%

Length

2021-09-28T14:28:22.153007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
78165000ao0008858
 
< 0.1%
92004000az0011746
 
< 0.1%
30189000em0022705
 
< 0.1%
78440000ar0388687
 
< 0.1%
62193000xb0006648
 
< 0.1%
06088000im0369576
 
< 0.1%
940330000i0670476
 
< 0.1%
06088000ob0437426
 
< 0.1%
76351000ei0024425
 
< 0.1%
920040000m0071401
 
< 0.1%
Other values (1535296)2453612
99.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ancien_id_parcelle
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)100.0%
Missing2459559
Missing (%)> 99.9%
Memory size75.1 MiB
440600000B1194

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row440600000B1194

Common Values

ValueCountFrequency (%)
440600000B11941
 
< 0.1%
(Missing)2459559
> 99.9%

Length

2021-09-28T14:28:22.403003image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-28T14:28:22.540006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
440600000b11941
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

numero_volume
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing2452794
Missing (%)99.7%
Memory size75.1 MiB

lot1_numero
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing1702571
Missing (%)69.2%
Memory size80.3 MiB

lot1_surface_carrez
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct16774
Distinct (%)8.0%
Missing2250280
Missing (%)91.5%
Infinite0
Infinite (%)0.0%
Mean62.17499336
Minimum0.3
Maximum7839
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.8 MiB
2021-09-28T14:28:22.725006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile16.33
Q134.61
median53.77
Q372.91
95-th percentile115.7115
Maximum7839
Range7838.7
Interquartile range (IQR)38.3

Descriptive statistics

Standard deviation108.4423874
Coefficient of variation (CV)1.744147953
Kurtosis2128.899575
Mean62.17499336
Median Absolute Deviation (MAD)19.15
Skewness38.30939309
Sum13011982.61
Variance11759.75138
MonotonicityNot monotonic
2021-09-28T14:28:23.059004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.5831
 
< 0.1%
12336
 
< 0.1%
15266
 
< 0.1%
10205
 
< 0.1%
13204
 
< 0.1%
20183
 
< 0.1%
65175
 
< 0.1%
40168
 
< 0.1%
64167
 
< 0.1%
63164
 
< 0.1%
Other values (16764)206581
 
8.4%
(Missing)2250280
91.5%
ValueCountFrequency (%)
0.31
 
< 0.1%
0.361
 
< 0.1%
0.41
 
< 0.1%
0.471
 
< 0.1%
0.53
< 0.1%
0.531
 
< 0.1%
0.581
 
< 0.1%
0.61
 
< 0.1%
0.631
 
< 0.1%
0.751
 
< 0.1%
ValueCountFrequency (%)
78391
 
< 0.1%
7688.710
< 0.1%
70001
 
< 0.1%
6947.851
 
< 0.1%
6696.721
 
< 0.1%
64713
 
< 0.1%
62501
 
< 0.1%
51621
 
< 0.1%
51531
 
< 0.1%
50002
 
< 0.1%

lot2_numero
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing2303817
Missing (%)93.7%
Memory size75.9 MiB

lot2_surface_carrez
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct11355
Distinct (%)22.2%
Missing2408519
Missing (%)97.9%
Infinite0
Infinite (%)0.0%
Mean64.32041045
Minimum0.21
Maximum8110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.8 MiB
2021-09-28T14:28:23.396008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.21
5-th percentile23.67
Q143.43
median61.06
Q376.1
95-th percentile110.39
Maximum8110
Range8109.79
Interquartile range (IQR)32.67

Descriptive statistics

Standard deviation83.64054797
Coefficient of variation (CV)1.300373355
Kurtosis5017.266439
Mean64.32041045
Median Absolute Deviation (MAD)16.45
Skewness61.88484225
Sum3282978.07
Variance6995.741264
MonotonicityNot monotonic
2021-09-28T14:28:23.698003image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69.550
 
< 0.1%
6047
 
< 0.1%
7240
 
< 0.1%
6639
 
< 0.1%
6737
 
< 0.1%
6436
 
< 0.1%
5036
 
< 0.1%
7136
 
< 0.1%
6535
 
< 0.1%
4834
 
< 0.1%
Other values (11345)50651
 
2.1%
(Missing)2408519
97.9%
ValueCountFrequency (%)
0.211
 
< 0.1%
0.32
 
< 0.1%
0.341
 
< 0.1%
0.41
 
< 0.1%
0.71
 
< 0.1%
0.751
 
< 0.1%
19
< 0.1%
1.031
 
< 0.1%
1.051
 
< 0.1%
1.061
 
< 0.1%
ValueCountFrequency (%)
81101
< 0.1%
72031
< 0.1%
6947.851
< 0.1%
6696.721
< 0.1%
6035.291
< 0.1%
29801
< 0.1%
2933.971
< 0.1%
27501
< 0.1%
2337.631
< 0.1%
2264.611
< 0.1%

lot3_numero
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing2434251
Missing (%)99.0%
Memory size75.1 MiB

lot3_surface_carrez
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct3726
Distinct (%)76.7%
Missing2454703
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean77.25787935
Minimum0.34
Maximum6947.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.8 MiB
2021-09-28T14:28:24.048005image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.34
5-th percentile13.1
Q141.04
median62.24
Q387.78
95-th percentile167.814
Maximum6947.85
Range6947.51
Interquartile range (IQR)46.74

Descriptive statistics

Standard deviation155.876137
Coefficient of variation (CV)2.01760828
Kurtosis1457.145336
Mean77.25787935
Median Absolute Deviation (MAD)23.01
Skewness34.54675199
Sum375241.52
Variance24297.3701
MonotonicityNot monotonic
2021-09-28T14:28:24.363006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46.837
 
< 0.1%
3117
 
< 0.1%
12.516
 
< 0.1%
1212
 
< 0.1%
31.512
 
< 0.1%
109
 
< 0.1%
138
 
< 0.1%
378
 
< 0.1%
157
 
< 0.1%
357
 
< 0.1%
Other values (3716)4724
 
0.2%
(Missing)2454703
99.8%
ValueCountFrequency (%)
0.342
 
< 0.1%
0.481
 
< 0.1%
0.811
 
< 0.1%
0.841
 
< 0.1%
0.891
 
< 0.1%
15
< 0.1%
1.011
 
< 0.1%
1.211
 
< 0.1%
1.441
 
< 0.1%
1.621
 
< 0.1%
ValueCountFrequency (%)
6947.851
< 0.1%
6696.721
< 0.1%
22401
< 0.1%
1468.32
< 0.1%
1089.91
< 0.1%
760.841
< 0.1%
7351
< 0.1%
715.81
< 0.1%
709.651
< 0.1%
691.241
< 0.1%

lot4_numero
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct622
Distinct (%)7.0%
Missing2450649
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean115.1531815
Minimum2
Maximum20027
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.8 MiB
2021-09-28T14:28:24.680528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q17
median22
Q369
95-th percentile430
Maximum20027
Range20025
Interquartile range (IQR)62

Descriptive statistics

Standard deviation507.3588243
Coefficient of variation (CV)4.405947086
Kurtosis503.2851738
Mean115.1531815
Median Absolute Deviation (MAD)17
Skewness18.19309495
Sum1026130
Variance257412.9766
MonotonicityNot monotonic
2021-09-28T14:28:24.979531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9564
 
< 0.1%
7513
 
< 0.1%
8486
 
< 0.1%
6478
 
< 0.1%
4467
 
< 0.1%
5418
 
< 0.1%
3262
 
< 0.1%
2157
 
< 0.1%
625140
 
< 0.1%
13136
 
< 0.1%
Other values (612)5290
 
0.2%
(Missing)2450649
99.6%
ValueCountFrequency (%)
2157
 
< 0.1%
3262
< 0.1%
4467
< 0.1%
5418
< 0.1%
6478
< 0.1%
7513
< 0.1%
8486
< 0.1%
9564
< 0.1%
119
 
< 0.1%
1272
 
< 0.1%
ValueCountFrequency (%)
200271
< 0.1%
170151
< 0.1%
117051
< 0.1%
91351
< 0.1%
83791
< 0.1%
74501
< 0.1%
74491
< 0.1%
74471
< 0.1%
71791
< 0.1%
70501
< 0.1%

lot4_surface_carrez
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct1037
Distinct (%)82.5%
Missing2458303
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean94.28529833
Minimum0.34
Maximum6947.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.8 MiB
2021-09-28T14:28:25.333531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.34
5-th percentile11.316
Q134.2
median64.43
Q395.75
95-th percentile196.464
Maximum6947.85
Range6947.51
Interquartile range (IQR)61.55

Descriptive statistics

Standard deviation290.1203116
Coefficient of variation (CV)3.077047183
Kurtosis463.8207384
Mean94.28529833
Median Absolute Deviation (MAD)30.43
Skewness20.40788883
Sum118516.62
Variance84169.79522
MonotonicityNot monotonic
2021-09-28T14:28:25.625526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.713
 
< 0.1%
3413
 
< 0.1%
34.312
 
< 0.1%
34.212
 
< 0.1%
3312
 
< 0.1%
78.67
 
< 0.1%
486
 
< 0.1%
94.26
 
< 0.1%
79.26
 
< 0.1%
66.66
 
< 0.1%
Other values (1027)1164
 
< 0.1%
(Missing)2458303
99.9%
ValueCountFrequency (%)
0.341
 
< 0.1%
0.481
 
< 0.1%
14
< 0.1%
1.561
 
< 0.1%
21
 
< 0.1%
2.121
 
< 0.1%
2.481
 
< 0.1%
3.151
 
< 0.1%
3.91
 
< 0.1%
41
 
< 0.1%
ValueCountFrequency (%)
6947.851
< 0.1%
6696.721
< 0.1%
22901
< 0.1%
1415.961
< 0.1%
891.91
< 0.1%
6761
< 0.1%
6631
< 0.1%
6501
< 0.1%
603.051
< 0.1%
592.12
< 0.1%

lot5_numero
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct463
Distinct (%)11.2%
Missing2455417
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean129.2302679
Minimum2
Maximum20028
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.8 MiB
2021-09-28T14:28:25.938526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q18
median26
Q374
95-th percentile385.6
Maximum20028
Range20026
Interquartile range (IQR)66

Descriptive statistics

Standard deviation559.3145453
Coefficient of variation (CV)4.328046009
Kurtosis476.6911203
Mean129.2302679
Median Absolute Deviation (MAD)20
Skewness17.59547083
Sum535401
Variance312832.7606
MonotonicityNot monotonic
2021-09-28T14:28:26.276528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9252
 
< 0.1%
8236
 
< 0.1%
7232
 
< 0.1%
5223
 
< 0.1%
6198
 
< 0.1%
4124
 
< 0.1%
3101
 
< 0.1%
268
 
< 0.1%
1758
 
< 0.1%
1957
 
< 0.1%
Other values (453)2594
 
0.1%
(Missing)2455417
99.8%
ValueCountFrequency (%)
268
 
< 0.1%
3101
< 0.1%
4124
< 0.1%
5223
< 0.1%
6198
< 0.1%
7232
< 0.1%
8236
< 0.1%
9252
< 0.1%
112
 
< 0.1%
122
 
< 0.1%
ValueCountFrequency (%)
200281
< 0.1%
117061
< 0.1%
91361
< 0.1%
71801
< 0.1%
70511
< 0.1%
60541
< 0.1%
60271
< 0.1%
51921
< 0.1%
50781
< 0.1%
50661
< 0.1%

lot5_surface_carrez
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct415
Distinct (%)73.7%
Missing2458997
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean104.5247247
Minimum1
Maximum6947.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.8 MiB
2021-09-28T14:28:26.776526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.8
Q127.535
median63.65
Q393.445
95-th percentile225.968
Maximum6947.85
Range6946.85
Interquartile range (IQR)65.91

Descriptive statistics

Standard deviation415.1813246
Coefficient of variation (CV)3.972087234
Kurtosis244.4511094
Mean104.5247247
Median Absolute Deviation (MAD)32.57
Skewness15.26434404
Sum58847.42
Variance172375.5323
MonotonicityNot monotonic
2021-09-28T14:28:27.082529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.836
 
< 0.1%
62.924
 
< 0.1%
78.912
 
< 0.1%
777
 
< 0.1%
236
 
< 0.1%
44.16
 
< 0.1%
66.86
 
< 0.1%
70.96
 
< 0.1%
91.96
 
< 0.1%
63.786
 
< 0.1%
Other values (405)448
 
< 0.1%
(Missing)2458997
> 99.9%
ValueCountFrequency (%)
12
 
< 0.1%
1.811
 
< 0.1%
1.821
 
< 0.1%
2.171
 
< 0.1%
2.371
 
< 0.1%
31
 
< 0.1%
3.53
 
< 0.1%
3.731
 
< 0.1%
3.91
 
< 0.1%
4.836
< 0.1%
ValueCountFrequency (%)
6947.851
< 0.1%
6696.721
< 0.1%
16801
< 0.1%
984.41
< 0.1%
552.41
< 0.1%
413.91
< 0.1%
411.821
< 0.1%
389.521
< 0.1%
388.211
< 0.1%
376.51
< 0.1%

nombre_lots
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct64
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3914602612
Minimum0
Maximum150
Zeros1702571
Zeros (%)69.2%
Negative0
Negative (%)0.0%
Memory size18.8 MiB
2021-09-28T14:28:27.408530image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum150
Range150
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7716914297
Coefficient of variation (CV)1.971314859
Kurtosis2639.730976
Mean0.3914602612
Median Absolute Deviation (MAD)0
Skewness22.11245427
Sum962820
Variance0.5955076627
MonotonicityNot monotonic
2021-09-28T14:28:27.682533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01702571
69.2%
1601246
 
24.4%
2130434
 
5.3%
316398
 
0.7%
44768
 
0.2%
51714
 
0.1%
6868
 
< 0.1%
7413
 
< 0.1%
8282
 
< 0.1%
11177
 
< 0.1%
Other values (54)689
 
< 0.1%
ValueCountFrequency (%)
01702571
69.2%
1601246
 
24.4%
2130434
 
5.3%
316398
 
0.7%
44768
 
0.2%
51714
 
0.1%
6868
 
< 0.1%
7413
 
< 0.1%
8282
 
< 0.1%
9159
 
< 0.1%
ValueCountFrequency (%)
1501
< 0.1%
1421
< 0.1%
1221
< 0.1%
1211
< 0.1%
1191
< 0.1%
1081
< 0.1%
1071
< 0.1%
931
< 0.1%
901
< 0.1%
802
< 0.1%

code_type_local
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing1143726
Missing (%)46.5%
Memory size118.9 MiB
1.0
528257 
2.0
402271 
3.0
298369 
4.0
86937 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0528257
21.5%
2.0402271
 
16.4%
3.0298369
 
12.1%
4.086937
 
3.5%
(Missing)1143726
46.5%

Length

2021-09-28T14:28:27.938528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-28T14:28:28.080525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0528257
40.1%
2.0402271
30.6%
3.0298369
22.7%
4.086937
 
6.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

type_local
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing1143726
Missing (%)46.5%
Memory size135.6 MiB
Maison
528257 
Appartement
402271 
Dépendance
298369 
Local industriel. commercial ou assimilé
86937 

Length

Max length40
Median length10
Mean length10.6819652
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMaison
2nd rowMaison
3rd rowAppartement
4th rowMaison
5th rowMaison

Common Values

ValueCountFrequency (%)
Maison528257
21.5%
Appartement402271
 
16.4%
Dépendance298369
 
12.1%
Local industriel. commercial ou assimilé86937
 
3.5%
(Missing)1143726
46.5%

Length

2021-09-28T14:28:28.274528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-28T14:28:28.443532image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
maison528257
31.8%
appartement402271
24.2%
dépendance298369
17.9%
local86937
 
5.2%
industriel86937
 
5.2%
commercial86937
 
5.2%
ou86937
 
5.2%
assimilé86937
 
5.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

surface_reelle_bati
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct3776
Distinct (%)0.4%
Missing1447059
Missing (%)58.8%
Infinite0
Infinite (%)0.0%
Mean115.0523535
Minimum1
Maximum218290
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.8 MiB
2021-09-28T14:28:28.693525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile23
Q151
median77
Q3107
95-th percentile190
Maximum218290
Range218289
Interquartile range (IQR)56

Descriptive statistics

Standard deviation853.1617471
Coefficient of variation (CV)7.415421944
Kurtosis21840.93601
Mean115.0523535
Median Absolute Deviation (MAD)27
Skewness121.3804483
Sum116490623
Variance727884.9667
MonotonicityNot monotonic
2021-09-28T14:28:28.994533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8020130
 
0.8%
6018268
 
0.7%
9018237
 
0.7%
7017733
 
0.7%
10015942
 
0.6%
5014507
 
0.6%
6513678
 
0.6%
4012877
 
0.5%
7512676
 
0.5%
12011683
 
0.5%
Other values (3766)856770
34.8%
(Missing)1447059
58.8%
ValueCountFrequency (%)
1265
 
< 0.1%
2407
 
< 0.1%
3139
 
< 0.1%
4153
 
< 0.1%
5150
 
< 0.1%
6255
 
< 0.1%
7364
 
< 0.1%
8584
 
< 0.1%
9820
 
< 0.1%
102405
0.1%
ValueCountFrequency (%)
2182903
< 0.1%
1517882
< 0.1%
1511571
 
< 0.1%
1433001
 
< 0.1%
1390401
 
< 0.1%
1370001
 
< 0.1%
1200051
 
< 0.1%
1120391
 
< 0.1%
1120371
 
< 0.1%
1113103
< 0.1%

nombre_pieces_principales
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct44
Distinct (%)< 0.1%
Missing1145290
Missing (%)46.6%
Infinite0
Infinite (%)0.0%
Mean2.501025664
Minimum0
Maximum109
Zeros385324
Zeros (%)15.7%
Negative0
Negative (%)0.0%
Memory size18.8 MiB
2021-09-28T14:28:29.328524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q34
95-th percentile6
Maximum109
Range109
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.112755955
Coefficient of variation (CV)0.8447558077
Kurtosis14.00198786
Mean2.501025664
Median Absolute Deviation (MAD)2
Skewness0.8089053007
Sum3287023
Variance4.463737726
MonotonicityNot monotonic
2021-09-28T14:28:29.628527image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0385324
 
15.7%
4229389
 
9.3%
3218427
 
8.9%
2158491
 
6.4%
5140307
 
5.7%
191864
 
3.7%
656124
 
2.3%
720780
 
0.8%
87803
 
0.3%
92992
 
0.1%
Other values (34)2769
 
0.1%
(Missing)1145290
46.6%
ValueCountFrequency (%)
0385324
15.7%
191864
 
3.7%
2158491
6.4%
3218427
8.9%
4229389
9.3%
5140307
 
5.7%
656124
 
2.3%
720780
 
0.8%
87803
 
0.3%
92992
 
0.1%
ValueCountFrequency (%)
1091
< 0.1%
971
< 0.1%
841
< 0.1%
711
< 0.1%
702
< 0.1%
552
< 0.1%
542
< 0.1%
511
< 0.1%
442
< 0.1%
411
< 0.1%

code_nature_culture
Categorical

HIGH CORRELATION
MISSING

Distinct27
Distinct (%)< 0.1%
Missing767675
Missing (%)31.2%
Memory size117.3 MiB
S
829111 
T
241955 
P
127873 
J
98241 
AB
83904 
Other values (22)
310801 

Length

Max length2
Median length1
Mean length1.189654734
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowT
2nd rowBT
3rd rowT
4th rowT
5th rowT

Common Values

ValueCountFrequency (%)
S829111
33.7%
T241955
 
9.8%
P127873
 
5.2%
J98241
 
4.0%
AB83904
 
3.4%
BT67375
 
2.7%
AG61910
 
2.5%
L58246
 
2.4%
VI26013
 
1.1%
BR24135
 
1.0%
Other values (17)73122
 
3.0%
(Missing)767675
31.2%

Length

2021-09-28T14:28:29.924526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
s829111
49.0%
t241955
 
14.3%
p127873
 
7.6%
j98241
 
5.8%
ab83904
 
5.0%
bt67375
 
4.0%
ag61910
 
3.7%
l58246
 
3.4%
vi26013
 
1.5%
br24135
 
1.4%
Other values (17)73122
 
4.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

nature_culture
Categorical

HIGH CORRELATION
MISSING

Distinct27
Distinct (%)< 0.1%
Missing767675
Missing (%)31.2%
Memory size137.2 MiB
sols
829111 
terres
241955 
prés
127873 
jardins
98241 
terrains a bâtir
83904 
Other values (22)
310801 

Length

Max length19
Median length4
Mean length6.577253773
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowterres
2nd rowtaillis simples
3rd rowterres
4th rowterres
5th rowterres

Common Values

ValueCountFrequency (%)
sols829111
33.7%
terres241955
 
9.8%
prés127873
 
5.2%
jardins98241
 
4.0%
terrains a bâtir83904
 
3.4%
taillis simples67375
 
2.7%
terrains d'agrément61910
 
2.5%
landes58246
 
2.4%
vignes26013
 
1.1%
futaies résineuses24135
 
1.0%
Other values (17)73122
 
3.0%
(Missing)767675
31.2%

Length

2021-09-28T14:28:30.168529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sols829111
40.6%
terres242037
 
11.8%
terrains145814
 
7.1%
prés129997
 
6.4%
jardins98241
 
4.8%
a83904
 
4.1%
bâtir83904
 
4.1%
taillis79111
 
3.9%
simples67375
 
3.3%
d'agrément61910
 
3.0%
Other values (24)223158
 
10.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

code_nature_culture_speciale
Categorical

HIGH CARDINALITY
MISSING

Distinct120
Distinct (%)0.1%
Missing2352011
Missing (%)95.6%
Memory size78.1 MiB
POTAG
28037 
PATUR
9387 
PIN
9344 
PARC
9316 
FRICH
6132 
Other values (115)
45333 

Length

Max length5
Median length5
Mean length4.511701643
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st rowJARD
2nd rowJARD
3rd rowJARD
4th rowJARD
5th rowJARD

Common Values

ValueCountFrequency (%)
POTAG28037
 
1.1%
PATUR9387
 
0.4%
PIN9344
 
0.4%
PARC9316
 
0.4%
FRICH6132
 
0.2%
VAOC4411
 
0.2%
CHAT3157
 
0.1%
CHENE2944
 
0.1%
MARAI2638
 
0.1%
ETANG2308
 
0.1%
Other values (110)29875
 
1.2%
(Missing)2352011
95.6%

Length

2021-09-28T14:28:30.456531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
potag28037
26.1%
patur9387
 
8.7%
pin9344
 
8.7%
parc9316
 
8.7%
frich6132
 
5.7%
vaoc4411
 
4.1%
chat3157
 
2.9%
chene2944
 
2.7%
marai2638
 
2.5%
etang2308
 
2.1%
Other values (110)29875
27.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

nature_culture_speciale
Categorical

HIGH CARDINALITY
MISSING

Distinct120
Distinct (%)0.1%
Missing2352011
Missing (%)95.6%
Memory size79.6 MiB
Jardin potager
28037 
Pâture plantée
9387 
Pins
9344 
Parc
9316 
Friche
6132 
Other values (115)
45333 

Length

Max length38
Median length14
Mean length12.27853351
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st rowJardin d'agrément
2nd rowJardin d'agrément
3rd rowJardin d'agrément
4th rowJardin d'agrément
5th rowJardin d'agrément

Common Values

ValueCountFrequency (%)
Jardin potager28037
 
1.1%
Pâture plantée9387
 
0.4%
Pins9344
 
0.4%
Parc9316
 
0.4%
Friche6132
 
0.2%
Vins d'appellation d'origine contrôlée4411
 
0.2%
Châtaigneraie3157
 
0.1%
Chênes2944
 
0.1%
Pré marais2638
 
0.1%
Etangs2308
 
0.1%
Other values (110)29875
 
1.2%
(Missing)2352011
95.6%

Length

2021-09-28T14:28:30.763526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
jardin29366
 
15.1%
potager28037
 
14.5%
pâture9387
 
4.8%
plantée9387
 
4.8%
pins9344
 
4.8%
parc9320
 
4.8%
friche6132
 
3.2%
ou5187
 
2.7%
vins4475
 
2.3%
d'appellation4411
 
2.3%
Other values (153)78967
40.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

surface_terrain
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED

Distinct40397
Distinct (%)2.4%
Missing767734
Missing (%)31.2%
Infinite0
Infinite (%)0.0%
Mean2862.548819
Minimum1
Maximum10723091
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.8 MiB
2021-09-28T14:28:31.060527image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile30
Q1226
median593
Q31710
95-th percentile11850
Maximum10723091
Range10723090
Interquartile range (IQR)1484

Descriptive statistics

Standard deviation16095.08664
Coefficient of variation (CV)5.622641799
Kurtosis134638.7225
Mean2862.548819
Median Absolute Deviation (MAD)465
Skewness245.0111668
Sum4842934518
Variance259051814
MonotonicityNot monotonic
2021-09-28T14:28:31.581534image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50034713
 
1.4%
100016077
 
0.7%
8005075
 
0.2%
6004844
 
0.2%
124536
 
0.2%
4004147
 
0.2%
1003964
 
0.2%
7003885
 
0.2%
3003870
 
0.2%
2003793
 
0.2%
Other values (40387)1606922
65.3%
(Missing)767734
31.2%
ValueCountFrequency (%)
13621
0.1%
22779
0.1%
32665
0.1%
42849
0.1%
52838
0.1%
62827
0.1%
72628
0.1%
82730
0.1%
92470
0.1%
103160
0.1%
ValueCountFrequency (%)
107230911
 
< 0.1%
60324391
 
< 0.1%
44286071
 
< 0.1%
34190001
 
< 0.1%
33832241
 
< 0.1%
15472501
 
< 0.1%
141152414
< 0.1%
12504351
 
< 0.1%
12412661
 
< 0.1%
12211051
 
< 0.1%

longitude
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct1389622
Distinct (%)57.2%
Missing30083
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean2.036064863
Minimum-63.141976
Maximum55.822271
Zeros0
Zeros (%)0.0%
Negative603739
Negative (%)24.5%
Memory size18.8 MiB
2021-09-28T14:28:31.902528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-63.141976
5-th percentile-2.071753
Q10.020138
median2.281216
Q33.945263
95-th percentile6.440334
Maximum55.822271
Range118.964247
Interquartile range (IQR)3.925125

Descriptive statistics

Standard deviation5.076373127
Coefficient of variation (CV)2.493227608
Kurtosis91.34359458
Mean2.036064863
Median Absolute Deviation (MAD)1.971133
Skewness-1.519063798
Sum4946572.754
Variance25.76956413
MonotonicityNot monotonic
2021-09-28T14:28:32.187530image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.972251858
 
< 0.1%
2.30474746
 
< 0.1%
4.33429707
 
< 0.1%
1.916957687
 
< 0.1%
1.849073648
 
< 0.1%
7.293616576
 
< 0.1%
2.36786505
 
< 0.1%
2.490008477
 
< 0.1%
7.210081426
 
< 0.1%
0.141625425
 
< 0.1%
Other values (1389612)2423422
98.5%
(Missing)30083
 
1.2%
ValueCountFrequency (%)
-63.1419764
< 0.1%
-63.1356242
 
< 0.1%
-63.1150441
 
< 0.1%
-63.1149773
< 0.1%
-63.111524
< 0.1%
-63.0929221
 
< 0.1%
-63.0874891
 
< 0.1%
-63.0867027
< 0.1%
-63.0861211
 
< 0.1%
-63.0859691
 
< 0.1%
ValueCountFrequency (%)
55.8222711
 
< 0.1%
55.8169134
< 0.1%
55.8149071
 
< 0.1%
55.8147471
 
< 0.1%
55.814651
 
< 0.1%
55.8134092
< 0.1%
55.8110782
< 0.1%
55.8104882
< 0.1%
55.8102822
< 0.1%
55.8097782
< 0.1%

latitude
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct1354320
Distinct (%)55.7%
Missing30083
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean46.65917365
Minimum-21.385203
Maximum51.081947
Zeros0
Zeros (%)0.0%
Negative7078
Negative (%)0.3%
Memory size18.8 MiB
2021-09-28T14:28:32.557525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-21.385203
5-th percentile43.312805
Q145.145914
median47.314022
Q348.803098
95-th percentile50.17572
Maximum51.081947
Range72.46715
Interquartile range (IQR)3.657184

Descriptive statistics

Standard deviation4.699261874
Coefficient of variation (CV)0.1007146399
Kurtosis135.1950247
Mean46.65917365
Median Absolute Deviation (MAD)1.578251
Skewness-10.18499257
Sum113357389.2
Variance22.08306216
MonotonicityNot monotonic
2021-09-28T14:28:32.864528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48.824982858
 
< 0.1%
48.915541747
 
< 0.1%
43.823295705
 
< 0.1%
48.983872687
 
< 0.1%
50.960173648
 
< 0.1%
43.718853576
 
< 0.1%
48.856387476
 
< 0.1%
43.669307426
 
< 0.1%
49.489123425
 
< 0.1%
48.921111401
 
< 0.1%
Other values (1354310)2423528
98.5%
(Missing)30083
 
1.2%
ValueCountFrequency (%)
-21.3852031
< 0.1%
-21.3851381
< 0.1%
-21.3850061
< 0.1%
-21.3840451
< 0.1%
-21.3837991
< 0.1%
-21.3837951
< 0.1%
-21.3836151
< 0.1%
-21.3836111
< 0.1%
-21.383561
< 0.1%
-21.3832981
< 0.1%
ValueCountFrequency (%)
51.0819474
< 0.1%
51.0817653
< 0.1%
51.081712
< 0.1%
51.0811271
 
< 0.1%
51.0811023
< 0.1%
51.0809421
 
< 0.1%
51.0807653
< 0.1%
51.0806273
< 0.1%
51.080594
< 0.1%
51.0802474
< 0.1%

Interactions

2021-09-28T14:25:52.043235image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:00.073277image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:16.121700image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:32.725424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:45.942276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:00.719416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:08.122797image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:14.415505image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:20.534077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:27.111478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:33.225324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:38.852160image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:45.586017image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:00.043986image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:10.633884image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:22.842546image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:35.790682image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:53.779282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:01.449275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:17.745401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:33.724438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:47.509370image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:01.588414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:08.522824image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:14.773514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:21.035073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:27.447478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:33.552340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:39.183157image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:47.095017image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:00.953995image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:11.573923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:23.973553image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:37.312351image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:54.816318image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:02.341335image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:18.959856image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:34.738458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:48.653442image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:02.013416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:08.901345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:15.098508image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:21.540072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:27.899174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:33.860337image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:39.503384image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:48.130106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:01.785172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:12.465935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:24.758551image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:38.326392image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:56.250336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:03.779646image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:20.620029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:35.814764image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:50.320609image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:02.506415image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:09.236433image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:15.402505image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:21.890077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:28.512173image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:34.166346image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:39.824383image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:49.532939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:02.605189image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:13.434015image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:25.946594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:39.902535image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:56.710480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:04.332765image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:21.068094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:36.266763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:50.755788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:02.939412image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:09.598963image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:15.669512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:22.194080image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:28.855180image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:34.463499image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:40.119429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:49.958359image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:02.956193image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:13.828079image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:26.271596image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:40.421536image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:57.027542image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:04.756783image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:21.451399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:36.674770image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:51.079830image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:03.286416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:10.045980image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:15.984507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:22.562075image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:29.155173image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:34.749505image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:40.393427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:50.265353image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:03.293191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:14.190080image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:26.694593image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:40.963542image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:57.320539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:05.127445image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:21.845419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:36.998765image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:51.411934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:03.684990image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:10.549960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:16.292515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:22.869076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:29.561175image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:35.060501image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:40.711457image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:50.584357image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:03.666188image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:14.503103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:26.981593image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:41.248553image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:57.689546image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:05.506443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:22.207481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:37.363765image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:51.714939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:03.996984image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:11.138968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:16.609511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:23.154074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:30.032177image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:35.354504image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-09-28T14:24:50.904354image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-09-28T14:24:11.857961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-09-28T14:24:30.650374image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-09-28T14:24:24.419071image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-09-28T14:24:36.474800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:41.968137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:51.846395image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-09-28T14:25:15.818413image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:25:28.069326image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-09-28T14:26:00.311762image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:08.023176image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:25.174913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:39.442790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:23:54.253299image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:05.423673image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:12.476960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:17.729507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-28T14:24:24.788115image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-09-28T14:25:50.351184image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-28T14:28:33.783527image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-28T14:28:34.365529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-28T14:28:35.040531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

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A simple visualization of nullity by column.
2021-09-28T14:26:54.600784image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-09-28T14:27:51.127363image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-09-28T14:28:02.843371image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

id_mutationdate_mutationnumero_dispositionnature_mutationvaleur_fonciereadresse_numeroadresse_suffixeadresse_nom_voieadresse_code_voiecode_postalcode_communenom_communecode_departementancien_code_communeancien_nom_communeid_parcelleancien_id_parcellenumero_volumelot1_numerolot1_surface_carrezlot2_numerolot2_surface_carrezlot3_numerolot3_surface_carrezlot4_numerolot4_surface_carrezlot5_numerolot5_surface_carreznombre_lotscode_type_localtype_localsurface_reelle_batinombre_pieces_principalescode_nature_culturenature_culturecode_nature_culture_specialenature_culture_specialesurface_terrainlongitudelatitude
02020-12020-01-071Vente8000.0NaNNaNFORTUNATB0631250.01072Ceyzériat1NaNNaN01072000AK0216NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNTterresNaNNaN1061.05.32354046.171919
12020-22020-01-021Vente2175.0NaNNaNTERRES DES CINQ SAULESB1241290.01203Laiz1NaNNaN012030000B0004NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNBTtaillis simplesNaNNaN85.04.89343646.251868
22020-22020-01-021Vente2175.0NaNNaNBOIS DU CHAMP RIONB0061290.01203Laiz1NaNNaN012030000B0173NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNTterresNaNNaN1115.04.89991946.235327
32020-22020-01-021Vente2175.0NaNNaNEN COROBERTB0251290.01203Laiz1NaNNaN012030000B0477NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNTterresNaNNaN1940.04.88234746.246519
42020-22020-01-021Vente2175.0NaNNaNTERRES DES CINQ SAULESB1241290.01203Laiz1NaNNaN012030000C0068NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNTterresNaNNaN1148.04.89468846.251820
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Last rows

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